1. Anemone Edge-based network management http://www.research.microsoft.com/projects/anemone/ Mort (Richard Mortier) Paul Barham, Austin Donnelly, Rebecca Isaacs

2. Over 700 people worldwide, spread through 6 research labs –Bangalore, Beijing, Cambridge, Redmond, San Francisco, Silicon Valley –Cover a wide range of CS and EE areas MSR Charter –Advance the state-of-the-art through cutting-edge research and publishing in the open literature –Provide competitive edge to Microsofts product groups through technology transfer and consultation –Engage with academic community through participation in conferences, programme committees, journal editorial boards, student thesis committees Cambridge lab is about 80 researchers, split into 4 main areas –Networking, systems, distributed systems Magpie, Topology discovery, Pastry, Avalanche, Vigilante, Anemone –Languages, security, theory –Graphics, vision, machine learning –Integrated systems, HCI, hardware Preamble: Microsoft Research

3. The process of monitoring and controlling a large complex distributed system of dumb devices where failures are common and resources scarce Networks are large: 10 5 hosts, 10 3 routers Networks are heterogeneous: 130 router hardware/OS combinations Networks run distributed protocols: OSPF, BGP, all very loosely synchronized Networks undergo continuous change: links fail and recover, upgrades occur Network management is hard!

4. State of the art? Tools to help visualize and inspect network 1.Get topology –Recursive use of ping and traceroute 2.Get traffic data –Routers using SNMP and NetFlow TM 3.Analyze and present the data –Wrap it all up in a GUI: triggers, graphs, top-10s, etc

5. Unfortunately… There are problems! Traffic is becoming more opaque to the network core –Increasing deployment of IPSec, tunnelling, encryption traceroute data is ambiguous and only polls the topology –Best case is the reverse path anyway SNMP data is often buggy –Non-critical part of router operation Routers are often resource starved –Not built using the latest CPU, memory technologies The result is that such systems can end up presenting inaccurate, untimely, incomplete data

6. Edge-based distributed network management platform Collect flow information from hosts, and Combine with topology information from routing protocols Enables applications Visualize current network state Analyse flow data for intrusion detection Simulate reconfiguration/failure for planning Control the network, automatically and in real-time Anemone

7. Demo overview emulates real-time per-host monitoring Link events Per-flow statistics Sample management application r e s u l t s OSPF packets captured from corporate network Synthetic traffic traces Load model Subnet list Continuous queries One-shot queries (topology, failure, recovery) (data transmitted) Anemone platform flow data from hosts + topology data from OSPF = distributed database computing load throughout network simulated for demo Data gathering Management applications

8. Benefits Anemone has a priori benefits over state of the art Visibility into opaque protocols –See into encrypted/tunnelled traffic e.g. IPSec, PPtP Plentiful resources at hosts –They need only deal with their own traffic Independence from poor quality data –No more reliance on SNMP and traceroute data

9. Where is my traffic going today? Anemone is a platform for network management apps Pictures of current topology and traffic –Routes+flows+forwarding rules BIG PICTURE In fact, where did my traffic go yesterday? –Keep historical data for capacity planning, etc A platform for anomaly detection –Historical data suggests normality, live monitoring allows anomalies to be detected Applications

10. Where might my traffic go tomorrow? Anemone enables what-if analysis Plug into a simulator back-end –Discrete event simulator or flow allocation solver Run multiple what-if scenarios –…failures –…reconfigurations –…technology deployments E.g. What happens to the network if we coalesce all the mail servers into one datacenter?

11. Applications Where should my traffic be going? Anemone helps close the control loop Use it to support an application that recomputes link weights to implement policy goals –Recomputation on the order of hours or days This enables more dynamic policies –Network configuration could be modified to track e.g. time of day/week/year load changes …potentially reducing bandwidth costs

12. Where are we now? Studying feasibility and building prototypes Three major components –Flow collection –Route collection –Anemone platform

13. Data collection: flows Synthesise flow data from low-level packet tracing Hosts track active flows –Using ETW, low overhead event posting infrastructure –Built prototype device driver provider & user-space consumer Took 24h packet traces from a client and a server –Peaks were at 165, respectively 5667, live flows per sec and 39, respectively 567, active flows per sec Quite manageable sized datasets

16. Interlude: OSPF routing 101 How does a packet get from any A to any B? Learn network topology; compute shortest paths For each node 1.Discover adjacencies (~immediate neighbours) 2.Advertise these link states to all other routers 3.Build link state database (~network topology) 4.Compute shortest paths to all destination prefixes 5.Forward to next-hop using longest-prefix-match (~most specific route)

17. Data collection: routes Passive collection of network critical control protocol OSPF is link-state so collect link state adverts Completely passive, modulo configuration Process data to recover network events and topology Data collected for (local, backbone) areas (20 days) –LSA DB size: (700, 1048) LSAs ~ (21, 34) kB –Event totals: (2526, 3238) events ~ (5.3, 6.7) evts/hr Small, generally stable with bursts of activity

18. NB: Spike to ~100 from initial DB collection truncated for readability

20. steady state complete dataset 10 mins: data ca. 25/Nov? 30 mins: LSRefreshTime? 35 mins: LSRefreshTime+CheckAge? 1–2 mins: RouterDeadInterval?

21. The Anemone platform Data unification, distribution and presentation Distributed database, logically containing 1.Traffic flow matrix (bandwidths), {srcs} × {dsts} Hosts can supply flows they source and sink Only need a subset of this data to get complete traffic matrix 2.…each entry annotated with current route, src to dst Note src/dst might be e.g. (IP end-point, application) OSPF supplies topology routes

22. System outline Control Packets Flows Routeing protocol Topology Visualize Simulate Simulator Anemone platform Traffic matrixSet of routes srcs dsts routes Hosts Routers

23. The Anemone platform Provides an API for presenting data Wish to be able to answer queries like –Who are the top-10 traffic generators? Easy to aggregate, dont care about topology –What is the load on link l ? Can aggregate from hosts, but need to know routes –What happens if we remove links {l … m} ? Interaction between traffic matrix, topology, even flow control Related work –{ distributed, continuous query, temporal } databases –Sensor networks, Astrolabe, SDIMS, PHI …

24. The Anemone platform Currently forming the core of the demo! Have simulation model –OSPF data gives topology, event list, routes –Simple load model to start with (load ~ # subnets) –Predecessor matrix (from SPF) reduces flow-data query set Where/what/how much to distribute/aggregate? –Is data read- or write-dominated? –Which is more dynamic, flow or topology data? –Can the system successfully self-tune?

25. The Anemone platform Many outstanding research questions Can we do as well/better than e.g. NetFlow TM ? –Accuracy of data vs. completeness of instrumentation Which data sets should we distribute and how? –Just OSPF data? Just flow data? A mixture? –Use DHTs? IP multicast? How many levels of aggregation? –How many nodes should a query touch? What sort of API is suitable? –Example queries for sample applications

26. http://www.research.microsoft.com/projects/anemone/ Building a coherent edge-based network management platform using flow monitoring and standard routeing protocols Applications include visualization, simulation, dynamic control Research issues include –Accuracy: will not be able to monitor 100% of traffic –Scalability: want to manage a 300,000 node network –Robustness: must work as nodes fail or network partitions –Control systems: use the data to optimize the network in real- time, as well as just observe and simulate

27. Backup slides SNMP Internet routeing Security

28. SNMP Protocol to manage information tables at devices Provides get, set, trap, notify operations –get, set: read, write values –trap: signal a condition (e.g. threshold exceeded) –notify: reliable trap Complexity mostly in the table design –Some standard tables, but many vendor specific –Non-critical, so often tables populated incorrectly

29. Internet routeing Q: how to get a packet from node to destination? A1: advertise all reachable destinations and apply a consistent cost function (distance vector) A2: learn network topology and compute consistent shortest paths (link state) –Each node (1) discovers and advertises adjacencies; (2) builds link state database; (3) computes shortest paths A1, A2: Forward to next-hop using longest-prefix-match

30. Security Threat: malicious/compromised host –Authenticate participants –Must secure route collector as if a router Threat: DoS on monitors –Difference between client under DoS and server? –Rate pace output from monitors Threat: eavesdropping –Standard IPSec/encryption solutions Have not considered cross-domain implications